Cambridge, Massachusetts-based Gunnari Auvinen is a principal software engineer at Labviva, where he has worked since 2020, leading code reviews, architectural planning, and system design sessions for highly technical systems. He previously served as a senior software engineer at Turo, where he helped migrate legacy JavaScript applications to a modern React/Redux stack, and earlier held full-stack engineering roles at Sonian. Auvinen earned his degree in electrical and computer engineering from Worcester Polytechnic Institute and began his career at General Dynamics Advanced Information Systems in 2005, working on programs across the country before settling in the Cambridge area. His technical background spans distributed systems and software architecture, giving him a practical vantage point on how artificial intelligence and machine learning tools are reshaping engineering and business operations alike.
Although the terms often appear interchangeably, critical differences exist between artificial intelligence (AI) and machine learning (ML). AI is a broader term that encompasses any machine designed to simulate intelligent behavior. Meanwhile, ML is a distinct subset of AI defined by algorithms that automatically improve as they gain more experience.
In other words, the two subjects are closely related yet differ notably in terms of scope and applications. It is important for software engineers to understand the differences between the two, as both AI and ML products grow more prevalent across industries.
These products can help business leaders process and evaluate huge sets of data, enhance decision-making, and access real-time metrics. That said, users must understand the different means of implementing AI and ML technology if they want to fully harness their abilities to create accurate forecasts and predictions.
Understanding the nature and complexities of AI is necessary for understanding ML operations. AI is an exceptionally broad field with many subsets. Any machine or computer that has the capacity for imitating human cognitive functions qualifies as AI.
Often, humans use AI technologies to see and understand information, assess data, and make recommendations, though these are only a few common uses of AI. All the AI technologies used by humans today fall under the banner of artificial narrow intelligence (ANI).
ANI products and platforms are relatively specialized in function–for example, image recognition technology. More advanced types of AI exist only in theory; these include artificial general intelligence, which can replicate human-level cognitive processing across limitless intellectual tasks, and artificial superintelligence, capable of superseding human intelligence.
As business leaders grow more comfortable with their understanding of AI and its potential to enhance operations, they can start to pursue specific knowledge, including ML applications. An important subset of AI, ML provides AI-powered machines and systems with the ability to become more accurate and efficient as they acquire more experience.
Rather than designers and coders explicitly programming and reprogramming a system, they can implement algorithms that collect and interpret large sets of data, learn from the information, and then provide users with informed recommendations. The more data users provide to ML algorithms, the better the results. Experts refer to this process as “training” the algorithms.
With each set of data, algorithms produce increasingly refined learning models, an example of autonomous AI learning. ML is similar to, and occasionally overlaps with, deep learning, natural language processing, and other prominent AI subfields.
Ideally, business leaders should establish a technology-friendly environment of innovation that allows workers to capitalize on the benefits of using AI and ML in tandem. Businesses of every size can unlock significant value through task automation, analytics, and a wide range of additional uses.
Perhaps most importantly, the combination of AI and ML enables leaders to make decisions with greater speed and efficiency, leading to operational improvements and reduced costs. Depending on the industry and structure of the business, leaders can focus on many AI and ML tools, including multimodal AI, agentic AI, diagnostic augmentation, and hyper-personalization and real-time optimization.
Enhanced personalization is one of the most important benefits of AI, as more than 80 percent of American consumers prefer businesses that provide customized experiences, driving customer retention by nearly 60 percent.
About Gunnari Auvinen
Gunnari Auvinen is a principal software engineer at Labviva in Cambridge, Massachusetts, a role he has held since 2020 after joining as senior technical lead. He previously worked as a senior software engineer at Turo and as a full-stack engineer at Sonian, and began his career at General Dynamics Advanced Information Systems in 2005. Auvinen holds an electrical and computer engineering degree from Worcester Polytechnic Institute and enjoys hiking, board games, and weightlifting in his free time.
